Datasets:

Snowflake
/

msmarco-v2.1-snowflake-arctic-embed-l

	# TREC RAG baselines using arctic-l and arctic-m-v1.5

	First, download the data including documents, queries, qrels.


	## Generate The doc and query embeddings

	```sh
	bash get_data.sh
	```

	Next, go ahead and convert the qrels format into json using the script below.

	```sh
	python convert_qrels_to_json.py
	```

	After that, go ahead and generate the query embeddings using the command below.

	```sh
	python generate_query_embeddings.py
	```

	After that, go ahead and generate embeddings for each shard. This will take ~ 20m per shard on a single H100. Feel free to parallelize. Make sure you have at least 600 gbs free.

	```sh
	python generate_doc_embeddings.py
	```


	## Retrieval Runs
	Once you have query and doc embeddings go ahead and retrieve. Given the size of the vectors we do this in shards. First we retrieve the top_n from each shard for each queryset on each shard. Feel free to parrelize.

	```sh
	python retrieve_from_shard.py <path to embeddings> <query_embedding_prefix> <shard> <num_retrieved> <use_faiss>
	```
	Alternatively, you can just run retrieve.sh in the background.

	```sh
	python merge_retrieved_shard.py <shard_retrieved_results> <output_filename> <top_n_docs> <qrel json> <metric to get per_query breakdown>
	```
	## Retrieval Scores


	### NDCG@10
	\| NDCG @10 \| \| \| \| \| \| \| \|
	\|--------------------\|--------\|----------------\|----------\|---------------\|---------------\|---------------\|---------------------------\|
	\| Dataset \| BM25 \| GTE-Large-v1.5 \| Arctic-L \| Arctic-M-V1.5 \| Arctic-M-V1.5 \| Arctic-M-V1.5 \| Cohere Embed3 - Trunc 128 \|
	\| Dim \| N/A \| 1024 \| 1024 \| 768 \| 256 \| 128 \| 128 \|
	\| Deep Learning 2021 \| 0.5778 \| 0.71928 \| 0.70682 \| 0.6936 \| 0.69392 \| 0.60578 \| 0.6962 \|
	\| Deep Learning 2022 \| 0.3576 \| 0.53576 \| 0.5444 \| 0.55199 \| 0.55608 \| 0.47348 \| 0.5396 \|
	\| Deep Learning 2023 \| 0.3356 \| 0.46423 \| 0.47372 \| 0.46963 \| 0.45196 \| 0.32789 \| 0.4473 \|
	\| msmarcov2-dev \| N/A \| 0.3538 \| 0.35844 \| 0.346 \| 0.34074 \| 0.28499 \| N/A \|
	\| msmarcov2-dev2 \| N/A \| 0.34698 \| 0.35821 \| 0.34518 \| 0.34339 \| 0.29606 \| N/A \|
	\| Raggy Queries \| 0.4227 \| 0.56782 \| 0.57759 \| 0.57439 \| 0.56686 \| 0.47555 \| N/A \|

	### Recall @100
	\| Recall@100 \| \| \| \| \| \| \| \|
	\|--------------------\|--------\|----------------\|----------\|---------------\|---------------\|---------------\|---------------------------\|
	\| Dataset \| BM25 \| GTE-Large-v1.5 \| Arctic-L \| Arctic-M-V1.5 \| Arctic-M-V1.5 \| Arctic-M-V1.5 \| Cohere Embed3 - Trunc 128 \|
	\| Dim \| N/A \| 1024 \| 1024 \| 768 \| 256 \| 128 \| 128 \|
	\| Deep Learning 2021 \| 0.3811 \| 0.4156 \| 0.41361 \| 0.43 \| 0.42245 \| 0.3488 \| 0.3914 \|
	\| Deep Learning 2022 \| 0.233 \| 0.31173 \| 0.31351 \| 0.32125 \| 0.3165 \| 0.26714 \| 0.3019 \|
	\| Deep Learning 2023 \| 0.3049 \| 0.35236 \| 0.34793 \| 0.37622 \| 0.36089 \| 0.28314 \| 0.3438 \|
	\| msmarcov2-dev \| 0.6683 \| 0.85135 \| 0.85131 \| 0.85435 \| 0.84985 \| 0.76201 \| N/A \|
	\| msmarcov2-dev2 \| 0.6771 \| 0.84333 \| 0.84767 \| 0.8576 \| 0.8526 \| 0.78987 \| N/A \|
	\| Raggy Queries \| 0.2807 \| 0.35125 \| 0.36228 \| 0.36915 \| 0.36149 \| 0.30272 \| N/A \|
	### Recall @1000

	\| Recall@1000 \| \| \| \| \| \| \| \|
	\|--------------------\|--------\|----------------\|----------\|---------------\|---------------\|---------------\|---------------------------\|
	\| Dataset \| BM25 \| GTE-Large-v1.5 \| Arctic-L \| Arctic-M-V1.5 \| Arctic-M-V1.5 \| Arctic-M-V1.5 \| Cohere Embed3 - Trunc 128 \|
	\| Dim \| N/A \| 1024 \| 1024 \| 768 \| 256 \| 128 \| 128 \|
	\| Deep Learning 2021 \| 0.7115 \| 0.73185 \| 0.7193 \| 0.74895 \| 0.73511 \| 0.63253 \| 0.7188 \|
	\| Deep Learning 2022 \| 0.479 \| 0.55174 \| 0.54566 \| 0.55413 \| 0.54499 \| 0.47823 \| 0.5558 \|
	\| Deep Learning 2023 \| 0.5852 \| 0.6167 \| 0.59577 \| 0.62262 \| 0.61199 \| 0.49188 \| 0.6025 \|
	\| msmarcov2-dev \| 0.8528 \| 0.93549 \| 0.93966 \| 0.94156 \| 0.94014 \| 0.87705 \| N/A \|
	\| msmarcov2-dev2 \| 0.8577 \| 0.93997 \| 0.93947 \| 0.94277 \| 0.94047 \| 0.91683 \| N/A \|
	\| Raggy Queries \| 0.5745 \| 0.63515 \| 0.63092 \| 0.64527 \| 0.63826 \| 0.55002 \| N/A \|